Feature Selection Analysis Using Quantum Machine Learning
K. Dinesh, S. Zuveriya, S. Rihaka +2 more
Feature extraction is the key in machine learning, which will affect the accuracy and efficiency of model. Due to the increasing complexity of the data, classical algorithms are often not powerful anymore. We introduce an approach combining QML with optimization algorithms to improve feature learning, using the computational power of quantum computers. Quantum Machine Learning (QML) is a new multidisciplinary field that integrates machine learning with quantum computing. on how quantum algorithms can enhance Machine Learning activities, such as Dimensionality reduction and optimization. In this paper we study the hybrid binary quantum-optimization algorithm and how optimization with quantum advantages can bring more efficient and robust feature extraction when used in conjunction with machine learning tasks. This Study also explores the use of hybrid binary quantum optimization methods for feature extraction.
CRC Press eBooks•Sep 10, 2026•0 citations